International Journal of Communication Systems最新文献

Radio resource management in a dynamic 5G HWAN, which includes several RATs like LTE and 5G NR, is examined in this work. A unique resource allocation strategy for 5G NR networks is proposed in this paper to overcome the practical drawbacks of centralized systems, such as signaling overhead and computing complexity. The challenge of resource allocation is tackled by framing it as a stochastic optimization dilemma. Addressing this challenge involves optimizing resource allocation using a novel hybrid optimization algorithm termed CPF-RPLO. It adopts a chaotic probability factor for decision-making in the CPF-RPLO that allows for adaptability in choosing between modified RPO and modified LOA. Each population in CPF-RPLO either chooses modified RPO or modified LOA for optimal resource allocation. This optimization process considers parameters such as path loss and throughput. To achieve the goal of maximizing average throughput utility while maintaining network stability, restrictions on these characteristics are imposed. The effectiveness of our proposed approach is demonstrated by theoretical analysis and simulation results, taking into account assumptions about network stability.

Frequency-selective surface (FSS) antennas are critical in modern communication systems, where optimizing their design for enhanced performance is essential. However, traditional methods often struggle with the complexity of FSS structures, leading to suboptimal designs. This paper addresses these limitations by proposing a novel CNN-GNN hybrid network (CGHN) framework for FSS antenna optimization. The proposed methodology integrates convolutional neural networks (CNNs) for efficient feature extraction of spatial patterns within FSS designs and graph neural networks (GNNs) to model the relational dependencies between unit cells. This approach ensures that both local features and global interactions are captured, leading to more accurate and optimized antenna designs. The objective is to enhance the performance of FSS antennas by leveraging the complementary strengths of CNNs and GNNs, with an emphasis on improving design accuracy and efficiency. The novelty lies in the combination of CNN's localized pattern recognition with GNN's relational learning, which together enable a comprehensive understanding of the antenna's behavior. The proposed CGHN framework achieves a 96.78% accuracy rate in predicting optimal FSS designs, with a 23.84% boost in performance due to CNN-driven feature extraction. Additionally, implementing stochastic gradient descent with gradient clipping increased the F1 score by 15%. Compared with existing techniques, the proposed method demonstrates significant improvements in both accuracy and efficiency, making it a superior choice for FSS antenna design optimization.

The energy-constrained nodes and tree-like topology of Low-Power and Lossy Networks (LLNs) frequently result in serious problems when energy bottlenecks occur. This occurrence affects the network performance in terms of node failure, lower connectivity, and higher energy consumption. This paper proposes a novel solution to these problems by integrating an Energy Efficient Multipath Routing Protocol (EM-RPL) with a Load-Based Energy Efficient RPL (LBEE-RPL) protocol. To address energy bottlenecks, the proposed EM-RPL protocol initiates the crucial mechanisms as subnode switching mechanism and an improved Trickle timer. The network's energy load is balanced by the subnode switching mechanism, which redistributes traffic away from overloaded nodes. The enhanced Trickle timer guarantees prompt identification and response to energy shortage conditions. This method enhances overall network performance while addressing the issue of data imbalance in energy consumption. An extensive simulation shows the proposed EM-RPL significantly increases network lifetime and energy efficiency. Specifically, integrating these protocols leads to a notable reduction in power consumption and a rise in the network's operational lifespan. The important problem of energy management in LLNs is effectively resolved by this work, which offers improved performance and sustainability for a range of Internet of Things (IoT) and related fields applications.

Leveraging Internet of Things (IoT) technologies in healthcare, including wireless sensor networks (WSN) and new generation networks, facilitates the integration of diverse medical equipment and enables smart interactions among them. This innovation contributes significantly in meeting the needs of healthcare professionals and improving the quality of life of patients. However, ensuring efficient communication within IoT systems is crucial to meet the critical demands of healthcare, such as real-time monitoring and emergency situations. This paper proposes a novel approach for determining cluster heads and selecting efficient paths in IoT-enabled healthcare applications. The cluster head selection process utilizes a fuzzy logic mechanism, considering factors like energy, distance, and latency. Subsequently, the particle swarm optimization (PSO) technique is employed to identify optimal pathways for routing. MATLAB-based simulations have been conducted to evaluate the proposed approach in terms of several key metrics, including average delay, packet delivery ratio, energy efficiency, and throughput. The results of the evaluation demonstrate significant improvements over comparable works. Specifically, our solution achieves a packet delivery ratio of 91.3%, an average delay of 0.12 s, a throughput of 60.1 bps, and an energy efficiency of 8.9 J/bit. These findings underscore the effectiveness of our proposed approach in meeting the stringent requirements of IoT-enabled healthcare systems, particularly by achieving lower delays and higher throughput.

Channel acquisitions are one of the most significant challenges to implementing reconfigurable intelligent surface (RIS)–assisted wireless networks. Basically, the base station (BS) and the mobile station (MS) are connected to one another via the RIS. However, accurate channel state information for each individual channel is required for the RIS to perform at its highest level. Therefore, effective execution of superresolution channel estimation (CE) at the BS to RIS, RIS to MS, and composed channel is necessary. Hence, this research proposed the MobileNet–long short-term memory (Mob-LSTM) technique for the RIS-aided mmWave MIMO system in order to provide an accurate CE model. In this research, three types of channels were initially developed: BS to RIS, RIS to MS, and composed channel. After that, these three types of channel parameters are estimated with the aid of the proposed Mob-LSTM model. Additionally, this research utilized a sequential weighting method, namely, a hybrid extended bald eagle (HEBE) optimizer, for fine-tuning the hyperparameters of the Mob-LSTM. Furthermore, the proposed research is implemented and examined using the MATLAB tool. In the simulation scenario, the proposed method can outperform the various existing approaches in terms of normalized mean square error (NMSE) and mean square error (MSE). Additionally, four different scenarios have been used to assess the proposed approach's efficiency: path gain analysis and convergence analysis of Mob-LSTM, MSE, and NMSE measures. According to the simulation outcomes, the suggested method attains a lower NMSE value of −52.53 and exceeds the existing techniques with high-CE effectiveness.

Energy conservation and optimized traffic delay are crucial challenges in Internet of Things (IoT) systems, particularly in wireless sensor networks (WSNs). This study presents a novel approach to address these issues through a fuzzy-based routing protocol. Traditional methods often struggle to effectively enhance routing protocols with optimized packet size control. Researchers have proposed a combination of machine learning and evolutionary techniques to overcome this limitation and enhance energy efficiency in WSNs. The new approach called MOSPFNN (Multi-Objective Spider Prey-localized Fuzzy Neural Network) leverages fuzzy logic control (FLC) to select optimal paths for traffic-aware multipath routing. Additionally, a new meta-heuristic algorithm of SP optimizer is employed to develop an energy-efficient spatial routing protocol with superior convergence and minimal local optima. The proposed protocol incorporates optimal FNN for congestion monitoring and traffic-aware routing. Simulation results validate that new protocol outperforms existing methods in terms of average end-to-end delay and packet delivery ratio (PDR). Using MOSPFNN, traffic with different priority levels can achieve a successful PDR rate of over 92% and network lifetime of above 2% than previous network within a reasonable timeframe. This research contributes to advancing energy-efficient and traffic delay-aware routing protocols in IoT systems.

In wireless sensor network (WSN) applications, the Received Signal Strength Indicator (RSSI) value from the original signal is determined for computing the distance between the unidentified and beacon nodes in the WSN. However, several factors including noise, diffraction, scattering, and some obstructions affect the precision of the localization techniques. This paper aims to implement a smart node localization scheme in WSNs by estimating the shortest distance between beacon nodes and unknown nodes using the RSSI factor. Initially, the beacon node is positioned at a known position, and the exact location of the unknown node is computed by the hybrid optimization concept. The objective of the proposed node localization method is to reduce the average localization error, and it is derived for assigning the unknown nodes to each beacon nodes. Optimization plays a vital role in providing clear communication among sensor nodes without any hindrance. The hybridized algorithm named as Fitness-aware Hybrid One-to-One with Archery Optimizer (FHOOAO) is used for the positioning of the unknown nodes to each beacon node. After assigning unknown nodes, their best positions are identified by considering the maximum number of hops. Finally, the experimentation is done in three different forms of node positioning in WSN such as S-shape, H-shape, and. C-shape. The simulation experiments demonstrate superior outcomes of the proposed model compared to alternative methods, and it also enhances communication efficiency among sensor nodes.

This article proposes a novel metasurface patch antenna with characteristics of metasurface and broadband for in-band full-duplex applications, which is configured with a parasitic metasurface, a square patch, two separated feeding networks for transmitting and receiving ports (Tx and Rx), and a square ground. Two feeding networks consist of an antisymmetric L-shaped probe feeding structure for Rx and a double F-shaped feeding structure with a 3 dB/180° hybrid coupler for the Tx port to achieve ±45° polarization and significantly enhanced isolation between the ports. Most importantly, the proposed parasitic metasurface realizes good impedance matching and improves realized gain. Therefore, the proposed antenna attains a −10 dB of overlapped bandwidth of 52.6% through 1.7 to 2.7 GHz with a high isolation of over 41.5 dB. Additionally, the measured gains are 8.9 ± 1.1 dBi for the Rx port and 7.4 ± 0.9 dBi for the Tx port, respectively. These characteristic data all support that the proposed antenna will play an essential role in IBFD applications.

The paper investigates the performance of a cyclic interleaved frequency division multiple access (CIFDMA) system proposed in our earlier works. In our present study, the carrier frequency offset (CFO) and the peak-to-average power ratio (PAPR) analysis are done for the proposed CIFDMA, supporting single and multiple data stream transmissions. The proposed system is analyzed without employing any compensation technique needed to alleviate the effect of CFO and PAPR. The CIFDMA delivers superior performance over the conventional IFDMA system under low and moderate CFOs. Besides, the bit error ratio (BER) versus signal-to-noise ratio (SNR) performance comparisons for constant and random CFOs reveal the dominance of the proposed system over the conventional system for both single and multiple data stream transmissions. The PAPR performance of the proposed system is superior to the orthogonal frequency division multiple access (OFDMA) system. However, the PAPR of the proposed system escalates by an acceptable margin to that of the conventional system, and the deviation in the PAPR value falls within the acceptable range. The simulation results conclude that the proposed CIFDMA system will better replace the conventional IFDMA system for next-generation communications.

In cognitive radio (CR) networks, the automatic modulation classification (AMC) is considered as the significant role in smart wireless communications. Due to the high growth of deep learning in the modern days, neural network-aided automated modulation categorization tasks have become highly demanded. Nevertheless, an enormous amount of attributes and the neural network's complexity make them complex to adopt in various scenarios. Moreover, the receiver systems are limited by the latency and less storage resources. Additionally, the detection system of the signal modulation is mostly hampered by overfitting issues and insufficient information. Therefore, a robust AMC model based on adaptive deep learning is implemented to determine the type of modulation used at the transmitter by observing the received signal. Initially, the necessary raw data for the suggested model is garnered from benchmark dataset. Also, the optimal features from the raw data are selected with the help of the fitness-revised position updating in Kookaburra optimization (FPUKO) for minimizing the computation time and enhancing the accuracy rates in the classification process. Moreover, this optimal feature selection makes the modulation selection process quick and efficient. Finally, the optimal features are fed to a cascaded and attention-based recurrent neural network (CA-RNN) in the modulation classification, which is designed to classify the type of modulation used on the transmitter side. Various experiments are conducted for the designed framework by comparing it with the existing models to view its efficiency.